Artificial immune system-based diagnostics and prognostics scheme and its experimental verification

Abstract

In this paper, a novel fault diagnostics and prediction (FDP) scheme is introduced by using artificial immune system (AIS) as an online approximator for a class of nonlinear discrete-time systems. Traditionally, AIS is considered as an offline tool for fault detection (FD). However, in this paper, AIS is utilized as an online approximator in discrete-time (OLAD) along with a robust adaptive term in the proposed fault diagnostics observer. Using the fact that the system outputs are alone measurable, an output residual is determined by comparing the observer and system outputs and a fault is detected if this output residual exceeds a predefined threshold. Upon detection, the OLADs are initiated to learn the unknown fault dynamics while the robust adaptive term ensure asymptotic convergence of the output residual for a state fault whereas a bounded result for an output fault. Additionally, for prognostics purposes, the parameter update law for AIS is used to estimate the time-to-failure (TTF). Finally, the performance of the proposed FDP scheme is demonstrated experimentally on an axial piston pump test-bed for two failure modes.

An artificial immune intelligent maintenance system for distributed industrial environments

“…Type of application AIS have been adopted to perform fault detection 12,13 and classification 14,15 using traditional methodologies or in combination with other techniques for fault diagnosis. 16 In some cases, AIS have been associated with fault forecasts and predictions 17,18 or have supported the optimisation of control strategies 9,19,20 System target AIS have been studied in various types of systems, including software, electronic and electric equipment 16,21,22 and mechanical components. 15,18,23,24 Recently, the approach has been applied to developing the maintenance of energy systems and smart grids [25][26][27]…”

“…Research studies underline that AIS can be easily scalable and thus can be applied without any constraints to a single part or equipment 17,18,23,29,36 rather than to a plant area 28,37,38…”

“…Regarding the system target area, the literature underlines that AIS applications can support a broad range of maintenance typologies. Mechanical devices, 14,15,18 electric motors 21 and electronic components 16 can be monitored using vibration, voltage, current and energy consumption to determine performance degradation. Moreover, AIS can be easily adopted to solve optimisation problems, including scheduling maintenance activities.…”